Method and system for identifying an individual in a crowd

ABSTRACT

The present disclosure relates to methods and systems for identifying an individual in a crowd. The method comprises the steps of capturing a plurality of crowd-viewing images using multiple image-capturing devices. For each image, a reference point within a facial contour of a person in the image is selected. The reference point is selected based on a facial feature on the facial contour of the person. The method further comprises the step of processing the image to obtain one or more angular measurements associated with a posture displayed by the person in the image. Based on the obtained angular measurements, location data of a predetermined body point of the person relative to the reference point is extracted.

TECHNICAL FIELD

The present disclosure relates broadly, but not exclusively, to a methodand system for identifying an individual in a crowd.

BACKGROUND ART

Surveillance is often used by government to monitor behaviour andactivities of people in public areas for prevention or investigation ofcrime. The number of surveillance cameras installed at public areas hasincreased substantially in recent years. Thus, surveillance can covermuch wider areas than before.

Typically, surveillance involves the tracking of a person's location.Facial features or clothing colours captured by surveillance cameras aresome examples of the traits that are compared for the purpose ofidentifying and tracking the location of a person. However, it is notedthat comparing the facial features and clothing colours may generatepoor results.

For example, faces of people captured by surveillance cameras may notalways be clear enough or in suitable angle to allow meaningfulcomparison. Also, it is noted that it could be difficult to extract thesame region on the clothing for comparison. It is usually assumed thatthe face and body of the person are in straight line in the image, andthe colour of the clothing directly below the face of the person inmultiple images captured by surveillance cameras are compared toidentify the person in the images. However, people may strike differentposes while walking or sitting down. As a result, a comparison based onfacial features or clothing colours as explained above may generate poorresults.

SUMMARY OF INVENTION Technical Problem

A need therefore exists to provide a method and system for identifyingan individual in a crowd that addresses at least one of the problemsabove or to provide a useful alternative.

Solution to Problem

According to a first aspect of the present disclosure, there is provideda method for identifying an individual in a crowd, the method comprisingthe steps of: capturing a plurality of crowd-viewing images usingmultiple image-capturing devices; for each image, selecting a referencepoint within a facial contour of a person in the image, wherein thereference point is selected based on a facial feature on the facialcontour of the person; processing the image to obtain one or moreangular measurements associated with a posture displayed by the personin the image; and

based on the obtained angular measurements, extracting location data ofa predetermined body point of the person relative to the referencepoint.

According to a second aspect of the present disclosure, there isprovided a system for identifying an individual in a crowd, the systemcomprising: multiple image-capturing devices configured to capture aplurality of crowd-viewing images; and a computer module incommunication with the multiple image-capturing devices, wherein thecomputer module is configured to: for each image, select a referencepoint within a facial contour of a person in the image, wherein thereference point is selected based on a facial feature on the facialcontour of the person; process the image to obtain one or more angularmeasurements associated with a posture displayed by the person in theimage; and based on the obtained angular measurements, extract locationdata of a predetermined body point of the person relative to thereference point.

BRIEF DESCRIPTION OF DRAWINGS

Embodiments of the present disclosure are provided by way of exampleonly, and will be better understood and readily apparent to one ofordinary skill in the art from the following written description and thedrawings.

FIG. 1 is shows a flow chart illustrating a method for identifying anindividual in a crowd.

FIG. 2 is shows images illustrating front views of a person with hisface in three different roll angles in accordance with an exampleembodiment.

FIG. 3 shows images illustrating front views of a person with his facein three different pan angles in accordance with an example embodiment.

FIG. 4 shows images illustrating front views of a person with his facein three different tilt angles in accordance with an example embodiment.

FIG. 5 shows an image illustrating a front view of a person with hisbody at a roll angle in accordance with an example embodiment.

FIG. 6 shows an image illustrating a front view of a person with hisbody at a roll angle in accordance with another example embodiment.

FIG. 7 shows a schematic diagram illustrating a computer suitable forimplementing the method and system of the example embodiments.

DESCRIPTION OF EMBODIMENTS

Embodiments of the present disclosure will be described, by way ofexample only, with reference to the drawings. Like reference numeralsand characters in the drawings refer to like elements or equivalents.

Some portions of the description which follows are explicitly orimplicitly presented in terms of algorithms and functional or symbolicrepresentations of operations on data within a computer memory. Thesealgorithmic descriptions and functional or symbolic representations arethe means used by those skilled in the data processing arts to conveymost effectively the substance of their work to others skilled in theart. An algorithm is here, and generally, conceived to be aself-consistent sequence of steps leading to a desired result. The stepsare those requiring physical manipulations of physical quantities, suchas electrical, magnetic or optical signals capable of being stored,transferred, combined, compared, and otherwise manipulated.

Unless specifically stated otherwise, and as apparent from thefollowing, it will be appreciated that throughout the presentspecification, discussions utilizing terms such as “capturing”,“selecting”, “processing”, “extracting”, “calculating”, “cropping”,“comparing”, “identifying” or the like, refer to the action andprocesses of a computer system, or similar electronic device, thatmanipulates and transforms data represented as physical quantitieswithin the computer system into other data similarly represented asphysical quantities within the computer system or other informationstorage, transmission or display devices.

The present specification also discloses apparatus for performing theoperations of the methods. Such apparatus may be specially constructedfor the required purposes, or may comprise a computer or other deviceselectively activated or reconfigured by a computer program stored inthe computer. The algorithms and displays presented herein are notinherently related to any particular computer or other apparatus.Various machines may be used with programs in accordance with theteachings herein. Alternatively, the construction of more specializedapparatus to perform the required method steps may be appropriate. Thestructure of a computer will appear from the description below.

In addition, the present specification also implicitly discloses acomputer program, in that it would be apparent to the person skilled inthe art that the individual steps of the method described herein may beput into effect by computer code. The computer program is not intendedto be limited to any particular programming language and implementationthereof. It will be appreciated that a variety of programming languagesand coding thereof may be used to implement the teachings of thedisclosure contained herein. Moreover, the computer program is notintended to be limited to any particular control flow. There are manyother variants of the computer program, which can use different controlflows without departing from the spirit or scope of the disclosure.

Furthermore, one or more of the steps of the computer program may beperformed in parallel rather than sequentially. Such a computer programmay be stored on any computer readable medium. The computer readablemedium may include storage devices such as magnetic or optical disks,memory chips, or other storage devices suitable for interfacing with acomputer. The computer readable medium may also include a hard-wiredmedium such as exemplified in the Internet system, or wireless mediumsuch as exemplified in the GSM mobile telephone system. The computerprogram when loaded and executed on such a computer effectively resultsin an apparatus that implements the steps of the preferred method.

In the following description, the term “crowd-viewing images” refers toimages that include pictures of one or more people and that are capturedby an image-capturing device.

In the following description, the term “facial contour” refers to anoutline of the face area of a person in the image captured by animage-capturing device.

In the following description, the term “image-capturing device” refersto an electronic camera that can capture images and/or videos of thecamera scene.

In the following description, the term “facial feature” refers to acharacteristic element of a face.

In the following description, the term “roll angle” refers to anorientation of the face or body of a person with respect to an X-axis ofa three-dimensional Cartesian coordinate system overlaid on the imagecaptured by the image-capturing device. Roll angle changes if the faceor body is tilted side-to-side.

In the following description, the term “tilt angle” refers to anorientation of the face or body of a person with respect to a Y-axis ofa three-dimensional Cartesian coordinate system overlaid on the imagecaptured by the image-capturing device. Tilt angle changes if the faceor body is lowered or raised in the forward and backward directions.

In the following description, the term “pan angle” refers to anorientation of the face or body of a person with respect to a Z-axis ofa three-dimensional Cartesian coordinate system overlaid on the imagecaptured by the image-capturing device. Pan angle changes if the face orbody is rotated sideways.

FIG. 1 shows a flow chart 100 illustrating a method for identifying anindividual in a crowd. At step 102, a plurality of crowd-viewing imagesis captured using multiple image-capturing devices. At step 104, areference point is selected within a facial contour of a person in eachimage captured. The reference point is selected based on a facialfeature on the facial contour of the person in the image. At step 106,the image is processed to obtain one or more angular measurementsassociated with a posture displayed by the person in the image. At step108, location data of a predetermined body point of the person relativeto the reference point is extracted based on the obtained angularmeasurements.

At step 102, a plurality of crowd-viewing images is captured usingmultiple image-capturing devices. In an embodiment, the image-capturingdevices are closed-circuit televisions (CCTVs) that are installed atpublic areas to monitor the public. Typically, digital images capturedby CCTVs are high-resolution images which can be used for surveillancepurposes. The crowd-viewing images captured by the CCTVs includepictures of one or more people. CCTVs are usually installed at placeswhere security is needed, such as a bank, a public gathering place, atraffic junction, and etc. The images captured by the CCTVs aretransmitted to a computer module for image processing.

It should be noted that CCTVs can also be video cameras that recordvideos of the camera scene. The recorded videos can be sent to thecomputer module to produce digital images which are used for furtheranalysis. In an embodiment, the digital images captured by the CCTVs areanalysed in real-time. This may advantageously allow dynamic analysis ofthe images captured by the CCTVs and timely actions to be taken in theprocess of tracking an individual. It will be appreciated that theanalysis of the images can also include processing of images retrievedfrom a computer database.

At step 104, a reference point within a facial contour of a person inthe image is selected. Specifically, the computer module processes theimage to identify a facial feature of a person in the image. Facialfeatures include characteristic elements of a face, such as eyes, ears,nose, and lips of a face. Based on the characteristic elements, areference point which meets a predetermined condition is selected. Forexample, the computer module is configured to select a point at adistance away from the one or more of the facial features as thereference point. In another example, the computer module is configuredto simply select one of the facial features as the reference point. Inan embodiment, the reference point is one or more selected from thefollowing: a middle point between both eyes of the person, one of theeyes of the person, a nose of the person, lips of the person, a middlepoint between both ears of the person, and one of the ears of theperson.

At step 106, the image is processed to obtain one or more angularmeasurements associated with a posture displayed by the person in theimage. The posture includes face and/or body orientations of the person.Angular measurements are angles that represent face and/or bodyorientations of the person in the image. People may strike differentpostures when walking or sitting down. Also, CCTVs may capture thepicture of a person at an angle, e.g. due to CCTVs being mounted at anelevated height above the ground. Thus, images captured by the CCTVs maynot include pictures of the person with the face and body facingdirectly to the CCTVs. If the face and/or body orientations in theplurality of images are not considered, the results generated fromcomparison of image data of those images would not be accurate.

In an embodiment, the computer module is configured to determine faceorientations of the person to obtain the relevant angular measurementsof the face, including the roll angle of the face, a pan angle of theface, and a tilt angle of the face. For example, the computer moduledetects facial features of the person in the image. The relativepositions of the facial features are used to obtain the angularmeasurements associated with the face orientation.

In a further embodiment, the computer module is configured to determinebody orientations of the person to obtain the relevant angularmeasurements of the body, including the roll angle of the body, a panangle of the body, and a tilt angle of the body. For example, thecomputer module detects body parts of the person (such as shoulder,waist, and limbs) in the image. The relative positions of the body partsare used to obtain the angular measurements associated with the bodyorientation.

It will be appreciated that the angular measurements associated withboth face and body orientations can be used together by the computermodule to determine the predetermined body point. By considering theangular measurements associated with the face and/or body orientations,the computer module can advantageously extract the accurate location ofthe predetermined body point and region in the image. In other words,the same part of the body region may be identified for the imagescaptured by the CCTVs. As a result, the method for identifying anindividual based on comparison of the predetermined body region in theimage can advantageously produce more precise results.

At step 108, location data of the predetermined body point relative tothe reference point is extracted based on the obtained angularmeasurements. In an embodiment, a Cartesian coordinate system isoverlaid on the image. The reference point has coordinates (x_(r),y_(r)) on the Cartesian coordinate system. The step of extracting thelocation data of the predetermined body point involves calculating thecoordinates of the predetermined body point (x_(p), y_(p)) relative tocoordinates of the reference points.

The calculation of the coordinates of the predetermined body point takesinto account the angular measurements associated with the face and/orbody orientations. Specifically, trigonometry functions are applied tothe angular measurements θ to determine adjustment coordinates (x_(θ),y_(θ)). In other words, the adjustment coordinates can be calculated ineach image using one or more trigonometry formulae that include angularmeasurements as the variables. The adjustment coordinates are used toobtain the coordinates of the predetermined body point, wherein thecoordinates of the predetermined body point (x_(p), y_(p)) is(x_(r)+x_(θ), y_(r)+y_(θ)). Examples of the trigonometry functions areexplained in further details below with respect to FIGS. 2-6.

Upon determining the coordinates of the predetermined body point, thepredetermined body region is cropped from the image based on theextracted location data. For example, the predetermined body region is aunit square with one of the corners being the coordinates of thepredetermined body point. Image data of predetermined body regioncropped from the plurality of images captured by multiple CCTVs arecompared. Based on the comparison of the image data, the computer moduledetermines whether the persons in the images are the same person. Thelocation of the individual can then be determined based on the locationof the CCTVs which captured the images.

FIG. 2 shows images 200 illustrating front views of a person with hisface in three different roll angles in accordance with an exampleembodiment.

The first image at the centre illustrates the person standing with hisface in an upright manner (roll angle θ_(r) of the face is 0°). Thesecond image on the left illustrates the person with his head tiltedsideways to his right side or the left side of one viewing the image(roll angle θ_(r) of the face is >0°). The third image at the rightillustrates the person with his head tilted sideways to his left side orthe right side of one viewing the image (roll angle θ_(r) of the face is<0°). No pan angle and tilt angle of the face are observed in theseimages 200. The Cartesian coordinate system includes an origin at thetop left corner of each image with an X-axis with positive X going rightand a Y-axis with positive Y going downwards.

In an example, it is assumed that the distance between both eyes is dunit and that the distance between the middle point and the lower neckis 3.5 d unit. The predetermined body point (x_(p), y_(p)) is at adistance of d unit directly below the lower neck of the person.

In this example, the middle point of the eyes is selected as a referencepoint. The coordinates of the reference point is (x_(r), y_(r)). Theimages 200 are processed to determine the roll angle r of the face.Subsequently, the coordinates of the predetermined body points (x_(p),y_(p)) is calculated in each image using this formula:

(x _(p) ,y _(p))=(x _(r)+[3.5 d*sin θ_(r)],y _(r) +d+[3.5 d*cos θ_(r)])

Predetermined body regions 202 are cropped from each of the images. Inthis example, the predetermined body region 202 is a square with the topleft corner of the square being the predetermined body point. The imagedata of the predetermined body regions 202 cropped from the images arecompared to determine if the persons in the images are the same person.

FIG. 3 shows images 300 illustrating front views of a person with hisface in three different pan angles in accordance with an exampleembodiment.

The first image at the centre illustrates the person standing with hisface in an upright manner (pan angle θ_(p) of the face is 0°). Thesecond image on the right illustrates the person with his head rotatedsideways to his left side or the right side of one viewing the image(pan angle θ_(p) of the face is >0°). The third image at the leftillustrates the person with his head rotated sideways to his right sideor the left side of one viewing the image (pan angle θ_(p) of the faceis <0°). No roll angle and tilt angle of the face are observed in theseimages 300. The Cartesian coordinate system includes an origin at thetop left corner of each image with an X-axis with positive X going rightand a Y-axis with positive Y going downwards.

In an example, it is assumed that the distance between both eyes is dunit and that the distance between the back and front of the head is 1.5d unit. The predetermined body point (x_(p), y_(p)) is at a distance ofd unit directly below the lower neck of the person.

In this example, the middle point of the eyes is selected as a referencepoint. The coordinates of the reference point is (x_(r), y_(r)). Theimages 300 are processed to determine the pan angle θ_(p) of the face.Subsequently, the coordinates of the predetermined body points (x_(p),y_(p)) is calculated in each image using this formula:

(x _(p) ,y _(p))=(x _(r)−[1.5 d*sin θ_(p)],y _(r)+3.5 d+d)

Predetermined body regions 302 are cropped from each of the images. Inthis example, the predetermined body region 302 is a square with the topleft corner of the square being the predetermined body point. The imagedata of the predetermined body regions 302 cropped from the images arecompared to determine if the persons in the images are the same person.

FIG. 4 shows images 400 illustrating front views of a person with hisface in three different tilt angles in accordance with an exampleembodiment.

The first image at the centre illustrates the person standing with hisface in an upright manner (tilt angle θ_(t) of the face is 0°). Thesecond image on the right illustrates the person with his head raised(tilt angle θ_(t) of the face is <0°). The third image at the leftillustrates the person with his head lowered (tilt angle θ_(t) of theface is >0°). No roll angle and pan angle of the face are observed inthese images 400. The Cartesian coordinate system includes an origin atthe top left corner of each image with an X-axis with positive X goingright and a Y-axis with positive Y going downwards.

In an example, it is assumed that the distance between both eyes is dunit and that the distance between the middle point and the lower neckis 3.5 d unit. The predetermined body point (x_(p), y_(p)) is at adistance of d unit directly below the lower neck of the person.

In this example, the middle point of the eyes is selected as a referencepoint. The coordinates of the reference point is (x_(r), y_(r)). Theimages 400 are processed to determine the tilt angle θ_(t) of the face.Subsequently, the coordinates of the predetermined body points (x_(p),y_(p)) is calculated in each image using this formula:

(x _(p) ,y _(p))=(x _(r) ,y _(r)+3.5 d*cos θ_(t) +d)

Predetermined body regions 402 are cropped from each of the images. Inthis example, the predetermined body region 402 is a square with the topleft corner of the square being the predetermined body point. The imagedata of the predetermined body regions 402 cropped from the images arecompared to determine if the persons in the images are the same person.

It should be noted that the roll, pan and tilt angle of the face may bedetermined. The coordinates of the predetermined body points (x_(p),y_(p)) may be calculated using this formula:

(x _(p) ,y _(p))=(x _(r)+3.5 d*sin θ_(r)−1.5 d*sin θ_(p) ,y _(r)+3.5d*cos θ_(r)+3.5 d*cos θ_(t) +d)

FIG. 5 shows an image 500 illustrating a front view of a person with hisbody at a roll angle in accordance with an example embodiment.Specifically, the image illustrates the person with his body rolling tohis left side, or the right side of one viewing the image. At thisorientation, the roll angle α_(r) of the body is <0°. The Cartesiancoordinate system includes an origin at the top left corner of the imagewith an X-axis with positive X going right and a Y-axis with positive Ygoing downwards.

In the example, it is assumed that the distance between both eyes is dunit and that the distance between the middle point and the lower neckis 3.5 d unit. The predetermined body point (x_(p), y_(p)) is at adistance of d unit directly below the lower neck of the person.

In this example, the middle point of the eyes is selected as a referencepoint. The coordinates of the reference point is (x_(r), y_(r)). Theimage 500 is processed to determine the roll angle α_(r) of the body.Subsequently, the coordinates of the predetermined body points (x_(p),y_(p)) is calculated using this formula:

(x _(p) ,y _(p))=(x _(r)+4.5 d*sin α_(r) ,y _(r)+4.5 d*cos α_(r))

FIG. 6 shows an image 600 illustrating a front view of a person with hisbody at a roll angle in accordance with another example embodiment.Specifically, the image illustrates the person with his body rolledsideways to his left side or the right side of one viewing the image(roll angle α_(r) of the body is <0°) and his face rotated sideways tohis left with only one eye visible in the image. In other words, panangle of the face is >0°. The Cartesian coordinate system includes anorigin at the top left corner of the image with an X-axis with positiveX going right and a Y-axis with positive Y going downwards.

In this example, the eye visible in the image is selected as a referencepoint with the coordinates of (x_(r), y_(r)). The formula explained withrespect to FIG. 5 can be used to calculate the coordinates of thepredetermined body points (x_(p), y_(p)) in this example in FIG. 6.

FIG. 7 depicts an exemplary computing device 700, hereinafterinterchangeably referred to as a computer system 700, where one or moresuch computing devices 700 may be used to identify an individual in acrowd. The exemplary computing device 700 can be used to implement themethod 100 shown in FIG. 1. The following description of the computingdevice 700 is provided by way of example only and is not intended to belimiting.

As shown in FIG. 7, the example computing device 700 includes aprocessor 707 for executing software routines. Although a singleprocessor is shown for the sake of clarity, the computing device 700 mayalso include a multi-processor system. The processor 707 is connected toa communication infrastructure 706 for communication with othercomponents of the computing device 700. The communication infrastructure706 may include, for example, a communications bus, cross-bar, ornetwork.

The computing device 700 further includes a main memory 708, such as arandom access memory (RAM), and a secondary memory 710. The secondarymemory 710 may include, for example, a storage drive 712, which may be ahard disk drive, a solid state drive or a hybrid drive, and/or aremovable storage drive 717, which may include a magnetic tape drive, anoptical disk drive, a solid state storage drive (such as a USB flashdrive, a flash memory device, a solid state drive or a memory card), orthe like. The removable storage drive 717 reads from and/or writes to aremovable storage medium 777 in a well-known manner. The removablestorage medium 777 may include magnetic tape, optical disk, non-volatilememory storage medium, or the like, which is read by and written to byremovable storage drive 717. As will be appreciated by persons skilledin the relevant art(s), the removable storage medium 777 includes acomputer readable storage medium having stored therein computerexecutable program code instructions and/or data.

In an alternative implementation, the secondary memory 710 mayadditionally or alternatively include other similar means for allowingcomputer programs or other instructions to be loaded into the computingdevice 700. Such means can include, for example, a removable storageunit 722 and an interface 750. Examples of a removable storage unit 722and interface 750 include a program cartridge and cartridge interface(such as that found in video game console devices), a removable memorychip (such as an EPROM or PROM) and associated socket, a removable solidstate storage drive (such as a USB flash drive, a flash memory device, asolid state drive or a memory card), and other removable storage units722 and interfaces 750 which allow software and data to be transferredfrom the removable storage unit 722 to the computer system 700.

The computing device 700 also includes at least one communicationinterface 727. The communication interface 727 allows software and datato be transferred between computing device 700 and external devices viaa communication path 726. In various embodiments of the disclosures, thecommunication interface 727 permits data to be transferred between thecomputing device 700 and a data communication network, such as a publicdata or private data communication network. The communication interface727 may be used to exchange data between different computing devices 700which such computing devices 700 form part an interconnected computernetwork. Examples of a communication interface 727 can include a modem,a network interface (such as an Ethernet card), a communication port(such as a serial, parallel, printer, GPIB, IEEE 1394, RJ45, USB), anantenna with associated circuitry and the like. The communicationinterface 727 may be wired or may be wireless. Software and datatransferred via the communication interface 727 are in the form ofsignals which can be electronic, electromagnetic, optical or othersignals capable of being received by communication interface 727. Thesesignals are provided to the communication interface via thecommunication path 726.

As shown in FIG. 7, the computing device 700 further includes a displayinterface 702 which performs operations for rendering images to anassociated display 750 and an audio interface 752 for performingoperations for playing audio content via associated speaker(s) 757.

As used herein, the term “computer program product” may refer, in part,to removable storage medium 777, removable storage unit 722, a hard diskinstalled in storage drive 712, or a carrier wave carrying software overcommunication path 726 (wireless link or cable) to communicationinterface 727. Computer readable storage media refers to anynon-transitory, non-volatile tangible storage medium that providesrecorded instructions and/or data to the computing device 700 forexecution and/or processing. Examples of such storage media includemagnetic tape, CD-ROM, DVD, Blu-Ray™ Disc, a hard disk drive, a ROM orintegrated circuit, a solid state storage drive (such as a USB flashdrive, a flash memory device, a solid state drive or a memory card), ahybrid drive, a magneto-optical disk, or a computer readable card suchas a PCMCIA card and the like, whether or not such devices are internalor external of the computing device 700. Examples of transitory ornon-tangible computer readable transmission media that may alsoparticipate in the provision of software, application programs,instructions and/or data to the computing device 700 include radio orinfra-red transmission channels as well as a network connection toanother computer or networked device, and the Internet or Intranetsincluding e-mail transmissions and information recorded on Websites andthe like.

The computer programs (also called computer program code) are stored inmain memory 708 and/or secondary memory 710. Computer programs can alsobe received via the communication interface 727. Such computer programs,when executed, enable the computing device 700 to perform one or morefeatures of embodiments discussed herein. In various embodiments, thecomputer programs, when executed, enable the processor 707 to performfeatures of the above-described embodiments. Accordingly, such computerprograms represent controllers of the computer system 700.

Software may be stored in a computer program product and loaded into thecomputing device 700 using the removable storage drive 717, the storagedrive 712, or the interface 750. The computer program product may be anon-transitory computer readable medium. Alternatively, the computerprogram product may be downloaded to the computer system 700 over thecommunications path 726. The software, when executed by the processor707, causes the computing device 700 to perform functions of embodimentsdescribed herein.

It is to be understood that the embodiment of FIG. 7 is presented merelyby way of example. Therefore, in some embodiments one or more featuresof the computing device 700 may be omitted. Also, in some embodiments,one or more features of the computing device 700 may be combinedtogether. Additionally, in some embodiments, one or more features of thecomputing device 700 may be split into one or more component parts.

When the computing device 700 is configured to identify an individual ina crowd, the computing system 700 will have a non-transitory computerreadable medium having stored thereon an application which when executedcauses the computing system 700 to perform steps comprising: capture aplurality of crowd-viewing images using multiple image-capturingdevices; for each image, select a reference point within a facialcontour of a person in the image, wherein the reference point isselected based on a facial feature on the facial contour of the person;process the image to obtain one or more angular measurements associatedwith a posture displayed by the person in the image; and based on theobtained angular measurements, extract location data of a predeterminedbody point of the person relative to the reference point.

It will be appreciated by a person skilled in the art that numerousvariations and/or modifications may be made to the present invention asshown in the specific embodiments without departing from the spirit orscope of the invention as broadly described. The present embodimentsare, therefore, to be considered in all respects to be illustrative andnot restrictive.

The whole or part of the exemplary embodiments disclosed above can bedescribed as, but not limited to, the following supplementary notes.

(Supplementary Note 1)

A method for identifying an individual in a crowd, the method comprisingthe steps of:

capturing a plurality of crowd-viewing images using multipleimage-capturing devices;

for each image, selecting a reference point within a facial contour of aperson in the image, wherein the reference point is selected based on afacial feature on the facial contour of the person;

processing the image to obtain one or more angular measurementsassociated with a posture displayed by the person in the image; and

based on the obtained angular measurements, extracting location data ofa predetermined body point of the person relative to the referencepoint.

(Supplementary Note 2)

The method as claimed in Supplementary Note 1, wherein the location datacomprises location coordinates in a Cartesian coordinate system overlaidon the image and wherein extracting the location data of thepredetermined body point comprises calculating the location coordinatesrelative to coordinates of the reference points.

(Supplementary Note 3)

The method as claimed in Supplementary Note 1 or 2, wherein the posturecomprises a face orientation of the person and wherein the angularmeasurements comprise at least one selected from a group consisting of:a roll angle of the face, a pan angle of the face, and a tilt angle ofthe face.

(Supplementary Note 4)

The method as claimed in any one of the preceding Supplementary Notes,wherein the posture comprises a body orientation of the person andwherein the angular measurements comprise at least one selected from agroup consisting of: a roll angle of the body, a pan angle of the body,and a tilt angle of the body.

(Supplementary Note 5)

The method as claimed in any one of the preceding Supplementary Notes,wherein the reference point comprises one or more selected from a groupconsisting of: a middle point between both eyes of the person, one ofthe eyes of the person, a nose of the person, lips of the person, amiddle point between both ears of the person, and one of the ears of theperson.

(Supplementary Note 6)

The method as claimed in any one of the preceding Supplementary Notes,further comprising the step of cropping a predetermined body regionbased on the extracted location data.

(Supplementary Note 7)

The method as claimed in Supplementary Note 6, further comprising thesteps of:

comparing image data of the predetermined body region cropped from theplurality of images captured by the multiple image-capturing devices;and

identifying the individual based on the comparison of the image data.

(Supplementary Note 8)

The method as claimed in any one of the preceding Supplementary Notes,wherein the plurality of images is real-time images.

(Supplementary Note 9)

A system for identifying an individual in a crowd, the systemcomprising:

multiple image-capturing devices configured to capture a plurality ofcrowd-viewing images; and

a computer module in communication with the multiple image-capturingdevices, wherein the computer module is configured to:

for each image, select a reference point within a facial contour of aperson in the image, wherein the reference point is selected based on afacial feature on the facial contour of the person;

process the image to obtain one or more angular measurements associatedwith a posture displayed by the person in the image; and

based on the obtained angular measurements, extract location data of apredetermined body point of the person relative to the reference point.

(Supplementary Note 10)

The system as claimed in Supplementary Note 9, wherein the location datacomprises location coordinates in a Cartesian coordinate system overlaidon the image and wherein the computer module is configured to calculatethe location coordinates relative to coordinates of the reference pointsto extract the location data of the predetermined body point.

(Supplementary Note 11)

The system as claimed in Supplementary Note 9 or 10, wherein the posturecomprises a face orientation of the person and wherein the angularmeasurements comprise at least one selected from a group consisting of:a roll angle of the face, a pan angle of the face, and a tilt angle ofthe face.

(Supplementary Note 12)

The system as claimed in any one of Supplementary Notes 9 to 11, whereinthe posture comprises a body orientation of the person and wherein theangular measurements comprise at least one selected from a groupconsisting of: a roll angle of the body, a pan angle of the body, and atilt angle of the body.

(Supplementary Note 13)

The system as claimed in any one of Supplementary Notes 9 to 12, whereinthe reference point comprises one or more selected from a groupconsisting of: a middle point between both eyes of the person, one ofthe eyes of the person, a nose of the person, lips of the person, amiddle point between both ears of the person, and one of the ears of theperson.

(Supplementary Note 14)

The system as claimed in any one of Supplementary Notes 9 to 13, whereinthe computer module is further configured to crop a predetermined bodyregion based on the extracted location data.

(Supplementary Note 15)

The system as claimed in Supplementary Note 14, wherein the computermodule is further configured to:

compare image data of the predetermined body region cropped from theplurality of images captured by the multiple image-capturing devices;and identify the individual based on the comparison of the image data.

(Supplementary Note 16)

The system as claimed in any one of Supplementary Notes 9 to 15, whereinthe plurality of images is real-time images.

This application is based upon and claims the benefit of priority fromSingapore patent application No. 10201802532Y, filed on Mar. 27, 2018,the disclosure of which is incorporated herein in its entirety byreference.

1. A method for identifying an individual in a crowd, the methodcomprising the steps of: capturing a plurality of crowd-viewing imagesusing multiple image-capturing devices; for each image, selecting areference point within a facial contour of a person in the image,wherein the reference point is selected based on a facial feature on thefacial contour of the person; processing the image to obtain one or moreangular measurements associated with a posture displayed by the personin the image; and based on the obtained angular measurements, extractinglocation data of a predetermined body point of the person relative tothe reference point.
 2. The method as claimed in claim 1, wherein thelocation data comprises location coordinates in a Cartesian coordinatesystem overlaid on the image and wherein extracting the location data ofthe predetermined body point comprises calculating the locationcoordinates relative to coordinates of the reference points.
 3. Themethod as claimed in claim 1, wherein the posture comprises a faceorientation of the person and wherein the angular measurements compriseat least one selected from a group consisting of: a roll angle of theface, a pan angle of the face, and a tilt angle of the face.
 4. Themethod as claimed in claim 1, wherein the posture comprises a bodyorientation of the person and wherein the angular measurements compriseat least one selected from a group consisting of: a roll angle of thebody, a pan angle of the body, and a tilt angle of the body.
 5. Themethod as claimed in claim 1, wherein the reference point comprises oneor more selected from a group consisting of: a middle point between botheyes of the person, one of the eyes of the person, a nose of the person,lips of the person, a middle point between both ears of the person, andone of the ears of the person.
 6. The method as claimed in claim 1,further comprising the step of cropping a predetermined body regionbased on the extracted location data.
 7. The method as claimed in claim6, further comprising the steps of: comparing image data of thepredetermined body region cropped from the plurality of images capturedby the multiple image-capturing devices; and identifying the individualbased on the comparison of the image data.
 8. The method as claimed inclaim 1, wherein the plurality of images is real-time images.
 9. Asystem for identifying an individual in a crowd, the system comprising:multiple image-capturing devices configured to capture a plurality ofcrowd-viewing images; and a computer module in communication with themultiple image-capturing devices, wherein the computer module isconfigured to: for each image, select a reference point within a facialcontour of a person in the image, wherein the reference point isselected based on a facial feature on the facial contour of the person;process the image to obtain one or more angular measurements associatedwith a posture displayed by the person in the image; and based on theobtained angular measurements, extract location data of a predeterminedbody point of the person relative to the reference point.
 10. The systemas claimed in claim 9, wherein the location data comprises locationcoordinates in a Cartesian coordinate system overlaid on the image andwherein the computer module is configured to calculate the locationcoordinates relative to coordinates of the reference points to extractthe location data of the predetermined body point.
 11. The system asclaimed in claim 9, wherein the posture comprises a face orientation ofthe person and wherein the angular measurements comprise at least oneselected from a group consisting of: a roll angle of the face, a panangle of the face, and a tilt angle of the face.
 12. The system asclaimed in claim 9, wherein the posture comprises a body orientation ofthe person and wherein the angular measurements comprise at least oneselected from a group consisting of: a roll angle of the body, a panangle of the body, and a tilt angle of the body.
 13. The system asclaimed in claim 9, wherein the reference point comprises one or moreselected from a group consisting of: a middle point between both eyes ofthe person, one of the eyes of the person, a nose of the person, lips ofthe person, a middle point between both ears of the person, and one ofthe ears of the person.
 14. The system as claimed in claim 9, whereinthe computer module is further configured to crop a predetermined bodyregion based on the extracted location data.
 15. The system as claimedin claim 14, wherein the computer module is further configured to:compare image data of the predetermined body region cropped from theplurality of images captured by the multiple image-capturing devices;and identify the individual based on the comparison of the image data.16. The system as claimed in claim 9, wherein the plurality of images isreal-time images.
 17. A processing apparatus for identifying anindividual in a crowd, the processing apparatus comprising: at least onememory storing instructions, and at least one processor configured toexecute the instructions to; select, for each of a plurality ofcrowd-viewing images captured by multiple image-capturing devices, areference point within a facial contour of a person in the image,wherein the reference point is selected based on a facial feature on thefacial contour of the person; process the image to obtain one or moreangular measurements associated with a posture displayed by the personin the image; and extract, based on the obtained angular measurements,location data of a predetermined body point of the person relative tothe reference point.